For the state-of-the-art deep learning models, training with a large quantity of data is important to prevent overfitting and achieve good generalization.
So, when there are multiple parties with each owning a dataset sampled from the same distribution,
pooling their datasets and training on the pooled dataset would yield an improved machine learning (ML) model for every participating party. For example, banks that use ML models to predict their customers' credit ratings [1]} would benefit from pooling their datasets as every bank can now train its ML model on a much larger dataset with more unique customers. This benefit would be even more pronounced in applications where data is difficult/costly to obtain and every party has limited data, such as in medical imaging [2]}.
